## LLM Agents Small library to build agents which are controlled by large language models (LLMs) which is heavily inspired by langchain. The goal was to get a better grasp of how such an agent works and understand it all in very few lines of code. Langchain is great, but it already has a few more files and abstraction layers, so I thought it would be nice to build the most important parts of a simple agent from scratch. Some more infos are in this Hacker News discussion from April 5th 2023 and the related blog post. ### How it works The agent works like this: * It gets instructed by a prompt which tells it the basic way to solve a task using tools * Tools are custom build components which the agent can use * So far, I've implemented the ability to execute Python code in a REPL, to use the Google search and to search on Hacker News * The agent runs in a loop of Thought, Action, Observation, Thought, ... * The Thought and Action (with the Action Input to the action) are the parts which are generated by an LLM * The Observation is generated by using a tool (for example the print outputs of Python or the text result of a Google search) * The LLM gets the new information appended to the prompt in each loop cycle and thus can act on that information * Once the agent has enough information it provides the final answer For more details on how it works, check out this blog post ### How to use it You can install this library locally by running: ``` pip install -r requirements.txt pip install -e . ``` inside it's directory after cloning it. You also need to provide the following env variables: * `OPENAI_API_KEY` to use the OpenAI API (obtainable at: https://platform.openai.com/account/api-keys) * `SERPAPI_API_KEY` to use the Google Search in case you use that tool (obtainable at: https://serpapi.com/) You can simply export them in bash like: `export OPENAI_API_KEY='sh-lsdf....'` Then you can run the script `python run_agent.py` and ask your question. To construct your own agent do it like this: ```python from llm_agents import Agent, ChatLLM, PythonREPLTool, HackerNewsSearchTool, SerpAPITool agent = Agent(llm=ChatLLM(), tools=[PythonREPLTool(), SerpAPITool(), HackerNewsSearchTool()]) result = agent.run("Your question to the agent") print(f"Final answer is {result}") ``` Of course, you can also build your custom tools or omit tools, for exmaple if you don't want to create a SERPAPI key.